Elif Ataseven

Dr. Elif Ataseven

orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 August 2025 in Diagnostics
Verified
Journal Article
Open access
PDF file
EDEmrah Dagdeviren
Emrah Dagdeviren
CTCan Tercan
Can Tercan
AYAli Selcuk Yneiocak
Ali Selcuk Yneiocak
BCBusra Cigdem
Busra Cigdem
EAElif Ataseven
Elif Ataseven
YKYücel Kaya
Yücel Kaya
Objectives: To assess the correlation and agreement of uterocervical angle (UCA) measurements obtained via transabdominal (TAUS), transperineal (TPUS), and transvaginal ultrasound (TVUS) between 16 and 24 weeks of gestation. Methods: In this prospective cross-sectional study, 136 pregnant women underwent UCA and cervical length (CL) assessments using TVUS, TAUS, and TPUS. All measurements were performed by a single experienced operator with an empty bladder. Correlation was assessed using Pearson analysis, while consistency and agreement were evaluated with intraclass correlation coefficient (ICC) and Bland–Altman plots. Results: Mean UCA values differed significantly between modalities (p < 0.001). Moderate correlation (r = 0.547) and consistency (ICC = 0.545) were found between TAUS and TVUS. The Bland–Altman analysis showed a systematic bias of –8° between TAUS and TVUS, with wide limits of agreement (–64° to +32.6°). TPUS showed a higher correlation with TVUS (r = 0.686), but poor consistency (ICC = 0.052), with broader limits of agreement (–32° to +49.2°). Conclusions: Although both TAUS and TPUS showed significant correlation with TVUS for UCA measurement, only TAUS demonstrated moderate consistency, suggesting that it should be used cautiously in clinical practice. TPUS demonstrated considerable variability in individual assessments and does not seem appropriate as a replacement for TVUS. Further studies are needed to validate these findings and improve measurement reliability.

8 September 2026 in Diagnostics
Verified
Journal Article
Open access
PDF file
ISIşık Sözen
Işık Sözen
IBIsil Turan Bakirci
Isil Turan Bakirci
EAElif Ataseven
Elif Ataseven
PUPiril Ustundag
Piril Ustundag
YOYahya Ozgun Oner
Yahya Ozgun Oner
IYIlkbal Temel Yuksel
Ilkbal Temel Yuksel
Background/Objectives: Distinguishing invasive cervical cancer from high-grade cervical intraepithelial neoplasia (CIN) before histopathology remains difficult. We evaluated routine blood-based parameters for this distinction and developed and internally validated a parsimonious prediction model, testing its incremental value over age. Methods: In this retrospective diagnostic accuracy study with two-gate (case–control) sampling, we analyzed 137 unique patients with histopathologically confirmed cervical cancer and 238 with high-grade CIN after duplicate removal. A four-variable model (age, CEA, neutrophil-to-lymphocyte ratio (NLR), and hemoglobin) was developed and internally validated by bootstrap optimism correction, calibration, and decision-curve analysis, with age-matched and subgroup sensitivity analyses. Results: The strongest single discriminators were age (AUC 0.826), C-reactive protein (0.805), and albumin (0.772). The continuous model showed an apparent AUC of 0.859 (optimism-corrected 0.853; calibration slope 0.962; Brier 0.136). The gain over age alone was statistically significant but modest (ΔAUC +0.027; DeLong p = 0.015). After 1:1 age matching, age no longer discriminated (AUC 0.49), whereas the model retained moderate discrimination (AUC 0.686), with NLR independent. Performance was lower for CIN 3 versus early-stage cancer (AUC 0.759). Conclusions: The model may provide adjunctive risk-stratification information, but clinical implementation requires external validation in prospectively assembled cohorts; the findings are hypothesis-generating.

10 February 2026 in Journal of Obstetrics and Gynaecology Research
Verified
Journal Article
Closed access
EDEmrah Dagdeviren
Emrah Dagdeviren
CTCan Tercan
Can Tercan
AYAli Selcuk Yneiocak
Ali Selcuk Yneiocak
NSNisa Sari
Nisa Sari
SBSeyda Buyuk
Seyda Buyuk
OAOnur Arabaci
Onur Arabaci